Interface  Description 

ContinuousInverseCumulativeProbabilityFunction 
Interface for a continuous distribution that can be sampled using
the
inversion method.

ContinuousSampler 
Sampler that generates values of type
double . 
DiscreteInverseCumulativeProbabilityFunction 
Interface for a discrete distribution that can be sampled using
the
inversion method.

DiscreteSampler 
Sampler that generates values of type
int . 
NormalizedGaussianSampler 
Marker interface for a sampler that generates values from an N(0,1)
Gaussian distribution.

Class  Description 

AhrensDieterExponentialSampler 
Sampling from an exponential distribution.

AhrensDieterMarsagliaTsangGammaSampler 
Sampling from the Gamma distribution.

BoxMullerGaussianSampler  Deprecated
since v1.1.

BoxMullerLogNormalSampler  Deprecated
since 1.1.

BoxMullerNormalizedGaussianSampler 
BoxMuller algorithm for sampling from Gaussian distribution with
mean 0 and standard deviation 1.

ChengBetaSampler 
Utility class implementing Cheng's algorithms for beta distribution sampling.

ContinuousUniformSampler 
Sampling from a uniform distribution.

DiscreteUniformSampler 
Discrete uniform distribution sampler.

GaussianSampler 
Sampling from a Gaussian distribution with given mean and
standard deviation.

InverseTransformContinuousSampler 
Distribution sampler that uses the
inversion method.

InverseTransformDiscreteSampler 
Distribution sampler that uses the
inversion method.

InverseTransformParetoSampler 
Sampling from a Pareto distribution.

LogNormalSampler 
Sampling from a lognormal distribution.

MarsagliaNormalizedGaussianSampler 
Marsaglia polar method for sampling from a Gaussian distribution
with mean 0 and standard deviation 1.

PoissonSampler 
Sampler for the Poisson distribution.

RejectionInversionZipfSampler 
Implementation of the Zipf distribution.

SamplerBase 
Base class for a sampler.

ZigguratNormalizedGaussianSampler 
Marsaglia and Tsang "Ziggurat" method for sampling from a Gaussian
distribution with mean 0 and standard deviation 1.

This package contains classes for sampling from statistical distributions.
As of version 1.0, the code for specific distributions was adapted from
the corresponding classes in the development version of "Commons Math" (in
package org.apache.commons.math4.distribution
).
When no specific algorithm is provided, one can still sample from any distribution, using the inverse method, as illustrated in:
Algorithms are described in e.g. Luc Devroye (1986), chapter 9 and chapter 10.Copyright © 2016–2018 The Apache Software Foundation. All rights reserved.